In conversations about the design, build or operation of infrastructure, the emphasis is often placed on the built environment and physical assets. Utmost thought is given to the thousands of roads, railways, bridges, and stations that connect our country and the huge amounts of economic and political capital that has gone into shaping them.
To design, operate and maintain the best possible infrastructure, we need accurate, instant, actionable data on its core purpose - the movement of people. To get that data, we must tap into the UK’s largest invisible and readily available sensor network - mobile network data
State of flux
Travel patterns are constantly changing, and the pandemic ripped up much of what we thought we knew about how people move as they go about their daily lives. Home working disrupted the traditional rush hour periods, and the role of train stations is shifting from solely transport interchanges to a blended community and retail space for locals working from home. This all provides a bit of a headache for transport planners as they aim to maximise the benefit of their next project, and transport operators who want to understand customer needs and optimise the service they offer them.
The stakes have arguably never been higher. For our railways, fluctuating and seemingly unpredictable passenger numbers have had an impact on revenue, with recent industrial action further stifling consumer confidence in rail’s reliability. At the top, major projects still draw debate on whether they are fit for purpose. On our highways, there is a huge demand to decarbonise our network. While sales of electric vehicles (EV) in the UK, for example, are booming, the national charging network is struggling to expand rapidly enough, with question marks over the optimal locations for installation. Across both networks, extreme weather has seen increased pressure on maintenance, with infrastructure owners desperate to understand where best to target their response, as passengers sit and wait.
It’s clear to get the best return on new projects or enhance our existing network, we need to understand how people move around the transport network and why. Unlike assets, people have hearts and minds and while we are creatures of habit, understanding our patterns can appear to be crystal ball gazing, or is it?
Vote with your feet
Mobile network data capability is one of the best kept secrets in data science and infrastructure. The UK’s largest invisible sensor network tracking location and movement information from every person’s mobile phone. The volume of data, from circa 60 million phones, (used within data privacy law), allows planners, operators and investors to review millions of real-life journeys to gather insights, where previously highly manual roadside interviews had to suffice.
Mobile network data exists already and involves no additional maintenance or infrastructure. If you wanted to create a network of millions of sensors from scratch, it would take huge amounts of time, effort and money and yet, we have this resource at our fingertips. One of the key benefits is flexibility and the ability to revaluate your data and map new trends and changes. Mobile network data is a continuous flow of movements, rather than samples captured from a certain period in time, meaning decision makers can keep pace with changing patterns and ‘the new normal’.
It can also provide hard evidence on where the best return is for large infrastructure projects. For example, mobile network data enabled Dorset County Council to identify the best sites where new housing could be built, based on thousands of genuine travel patterns throughout the County. This all at a considerable cost saving compared to traditional manual roadside surveys. Not only that, mobile network data helps to democratise the planning process by providing schemes that genuinely reflect real life travel preferences and patterns, giving confidence to the public that transport decisions are being taken in their interest.
The most exciting possibility is that mobile network data does not exist in a vacuum. When combined with our existing technology, there is a possibility to be something genuinely revolutionary. By embedding a greater understanding of passenger movements into our existing operational platforms, we can better understand the true impact of infrastructure operations on passengers. The potential is to deliver radical change to how our clients manage widescale disruption. A refined, near real-time understanding of passenger information would drastically increase the customer experience operators are able to provide during severe weather or other outages.
Mobile network data can provide us with a picture of where people move to in the event of delays, and dynamically balance loading across networks: allowing interventions to minimise total disruption, better integrating services and providing a more holistic lens to infrastructure operation.
Reimagining our communities
Mobile network data will give us the opportunity to reimagine and redesign the transport networks and stations of the future. Not only can mobile network data be used to enhance the digital capability of master planning, but we can also use it at a more forensic level. An understanding of the thousands of individual journeys at every station will allow us to enhance customer experience, optimise our asset use and even create greater commercialisation opportunities. This will provide huge value to station owners in their bid to react and plan to increasingly unpredictable passenger numbers. For our EV rollout, the use of mobile network data will enhance demand analysis and provide real travel pattern data, where previously planners could only rely on outdated and unreliable census information.
Ultimately, mobile network data provides high value, accurate and instant data that can provide genuine evidence and insight for transport operations and major schemes. And if we want to keep pace with changing customer expectations, we must ditch the crystal ball and put people’s real movements at the heart of decision making.
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